Training dataset.
We conduct our training process on the KITTI dataset [1]}. It provides the odometry benchmark containing 22 sequences of point cloud data, which are collected by LiDAR sensors Velodyne HDL-64E, covering various road scenes. For each sequence, we partition every 3 consecutive frames into a triplet. With regard to each triplet, the first and third frames are as the input frames of our model, while the second frame is the ground-truth frame. In this way, 16 sequences (10327 triplets) are chosen as the training dataset. Among the others, 3 sequences (2400 triplets) are for validation dataset, while 3 sequences (1784 triplets) are for test dataset. It is worthwhile to noting that each dataset covers all kinds of scenes.
